On the impact of the choice of the evapotranspiration equation in a crop model : climate data error propagation and climate change impact projection

International audienceAs it enables the understanding and the quantification of the transfer of water in ecosystems and from ecosystems to the atmosphere, evapotranspiration is a key component to assess climate impact on hydrology and agriculture. In crop models, the estimation of the evapotranspiration rate requires first calculating potential or reference evapotranspiration from climate data. To compute reference evapotranspiration different formulas requiring more or less climate data are used. The choice of the formulation of this key process is very likely to have an impact on calculated crop yield. The FAO recommends using the Penman-Monteith (PM) equation if all the climate data required for this equation are available and using Hargreaves (H) equation when climate data, especially net radiation, are missing. The Priestley-Taylor equation is also widely used in crop models. Which of these equations is the most accurate when all the climate data required are available but contain errors ? Does the choice of the evapotranspiration equation have an impact on crop yield projection in a context of climate change ? Does the use of some equations induce more pessimistic crop yield projection ? We studied the impact of the reference evapotranspiration equations on simulated crop yield using climate data with errors. 4 equations (PM, H and 2 versions of the Priestley-Taylor equation - PT) were tested simulating pearl millet over 12 stations in Senegal. In this case, we found that the use of a PT equation may introduce a percent mean bias error of more than -35% on simulated crop yield while it is limited to 2% when using the H equation. The influence of the evapotranspiration equation on the quantification of climate change impact on crop yield is examined applying the AgMIP C3MP protocol over the 12 stations in Senegal then analyzing ISI-AgMIP GGCM Intercomparison fast-track project outputs over the world. Our preliminary results show that crop yields computed using a PT equation are more sensitive (crop yield reduced by 31% on average) to temperature change than PM and H (-23%) equations and this for each of the 12 stations considered in Senegal

Impact of ET₀ method on the simulation of historical and future crop yields: a case study of millet growth in Senegal

The reference evapotranspiration (ET0) is an integrated climatic variable from which many crop models derive simulated crop yields. In most of these models, different equations are parameterized leaving the choice of the equation to the user. However, the impact of the choice of the ET0 equations on crop yield prediction has been little studied.The present study proposes a sensitivity analysis of the impact of the choice of the ET0 equation on simulated millet yields using SARRA‐H crop model over 12 Senegalese stations representative of the Sudano‐Sahelian climate conditions of West Africa.Priestley‐Taylor, a modified Priestley‐Taylor and Hargreaves equations lead to simulated yields up to 19% than those calculated using the Penman‐Monteith equation. Despite high biases in wind speed, among the tested methods, the Penman‐Monteith method remains the most robust to derive ET0 and yield over the major part of Senegal, Hargreaves equation being more appropriated under dry climates. The choice of ET0 formulation introduces uncertainties representing 8% of baseline yield regardless of precipitation changes; for wet conditions these uncertainties approach 30% of the overall climate change impact. The choice of ET0 equation is increasingly important, with local temperature changes out to 4 °C, while extreme changes above 6 °C depend less on the ET0 equation

Are satellite based rainfall estimates accurate enough for crop modelling under Sahelian climate?

International audienceAgriculture is considered as the most climate dependant human activity. In West Africa and especially in the sudano-sahelian zone, rain-fed agriculture - that represents 93% of cultivated areas and is the means of support of 70% of the active population - is highly vulnerable to precipitation variability. To better understand and anticipate climate impacts on agriculture, crop models - that estimate crop yield from climate information (e.g rainfall, temperature, insolation, humidity) - have been developed. These crop models are useful (i) in ex ante analysis to quantify the impact of different strategies implementation - crop management (e.g. choice of varieties, sowing date), crop insurance or medium-range weather forecast - on yields, (ii) for early warning systems and to (iii) assess future food security. Yet, the successful application of these models depends on the accuracy of their climatic drivers. In the sudano-sahelian zone , the quality of precipitation estimations is then a key factor to understand and anticipate climate impacts on agriculture via crop modelling and yield estimations. Different kinds of precipitation estimations can be used. Ground measurements have long-time series but an insufficient network density, a large proportion of missing values, delay in reporting time, and they have limited availability. An answer to these shortcomings may lie in the field of remote sensing that provides satellite-based precipitation estimations. However, satellite-based rainfall estimates (SRFE) are not a direct measurement but rather an estimation of precipitation. Used as an input for crop models, it determines the performance of the simulated yield, hence SRFE require validation. The SARRAH crop model is used to model three different varieties of pearl millet (HKP, MTDO, Souna3) in a square degree centred on 13.5°N and 2.5°E, in Niger. Eight satellite-based rainfall daily products (PERSIANN, CMORPH, TRMM 3b42-RT, GSMAP MKV+, GPCP, TRMM 3b42v6, RFEv2 and EPSAT-SG) are integrated using a crop model, then compared and tested against simulations obtained using in situ data. As in situ data, kriged rain gauge measurements are computed from about 50 rain gauges within the square degree. We show that direct use of SRFE does not reproduce the yield variability obtained from in situ observations. In a second time, different satellite products errors (e.g. annual bias, accuracy at the beginning of the rainy season) are corrected before yield modelling to assess their impact on crop yield simulation and to be able to know which improvement in SRFE will be useful for crop yield estimation. We show that corrected satellite products enable a better yield variability representation and that error correction does not have the same impact on the different varieties computed. Finally, simulated yield quality versus precipitations temporal resolution is assessed - as well as SRFE accuracy versus SRFE temporal resolution - in order to sort out the best agreement between temporal resolution and SRFE accuracy

Rainfall missing values filling : impacts on crop yield estimation in the soudano-sahelian zone

National audienceLes précipitations constituent le principal facteur limitant les rendements agricoles en Afrique de l'Ouest. L'étude des rendements agricoles à l'aide de modèles de croissance de plante est donc particulièrement sensible à la qualité des données de précipitations utilisées. Les pluies mesurées in situ permettent de disposer de longues séries temporelles mais présentent plusieurs limites dont la présence d'un nombre souvent important de lacunes. L'impact, sur les rendements en mil, de l'utilisation de différentes méthodes de comblement des pluies est étudié ici pour des taux de lacune de 10 % et 20 %. Il en ressort que lorsque les lacunes sont correctement comblées, celles-ci n'ont pas une incidence importante sur la simulation de rendements. Actes en ligne http://www.climato.be/aic/colloques/actes/grenoble2012_actes.pd

Errors and uncertainties introduced by a regional climate model in climate impact assessments: example of crop yield simulations in West Africa

16 pagesInternational audienceThe challenge of estimating the potential impacts of climate change has led to an increasing use of dynamical downscaling to produce fine spatial-scale climate projections for impact assessments. In this work, we analyze if and to what extent the bias in the simulated crop yield can be reduced by using the Weather Research and Forecasting (WRF) regional climate model to downscale ERA-Interim (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) rainfall and radiation data. Then, we evaluate the uncertainties resulting from both the choice of the physical parameterizations of the WRF model and its internal variability. Impact assessments were performed at two sites in Sub-Saharan Africa and by using two crop models to simulate Niger pearl millet and Benin maize yields. We find that the use of the WRF model to downscale ERA-Interim climate data generally reduces the bias in the simulated crop yield, yet this reduction in bias strongly depends on the choices in the model setup. Among the physical parameterizations considered, we show that the choice of the land surface model (LSM) is of primary importance. When there is no coupling with a LSM, or when the LSM is too simplistic, the simulated precipitation and then the simulated yield are null, or respectively very low; therefore, coupling with a LSM is necessary. The convective scheme is the second most influential scheme for yield simulation, followed by the shortwave radiation scheme. The uncertainties related to the internal variability of the WRF model are also significant and reach up to 30% of the simulated yields. These results suggest that regional models need to be used more carefully in order to improve the reliability of impact assessments

Rising ponds in uncultivated Sahel: A delayed effect of drought, involving plant dynamics and soil erosion

International audienceIt is now well accepted that runoff has increased in the Sahel during the recent multi-decadal drought period. There is still a debate on the causes of such an increase, and especially on the role of crop area expansion, which took place over the same period of time in part of the Sahel. In 2010, Gardelle et collaborators showed that ponds' surface in uncultivated Sahel exhibited a dramatic increase in the Gourma area (Mali) over the 1972-2006 period (Gardelle et al. 2010). A following study by Ramarohetra and collaborators (unpublished) established that a rapid evolution of both vegetation and soil types occurred over a small catchment already studied in 1954-56, the Tin Adjar catchment, also located in Sahelian Mali. This catchment underwent a strong increase in rocky outcrops and hardpans areas and a strong decreased in loamy soils and associated vegetation. During the same period, gullies significantly increased in length, number and order, whereas temporary pans (in 1954) were drained by gullies in 2008. Sand was removed from the highest slopes of the catchment and deposited in the lowest parts. The mechanism proposed to explain such changes involves a decrease in vegetation cover (mainly herbaceous) caused by the lack of precipitation in the driest years (70' and 80'), which favored the concentration of runoff in gullies at the expense of sheet runoff. In turn, the concentration of runoff further deprived plants from water. Such a mechanism takes place over shallow soils and is not related to cultivation. Such shallow soils occupy approximately 30 % of the Gourma and are found in other areas of the Sahel. Whether such a phenomenon also took place in other part of the Sahel has been further investigated with series of Landsat images. Ponds in the 70' and 2000' were classified in potentially affected areas of Mauretania, Mali and Niger. Rising ponds in part of uncultivated Sahel is shown to be an important, delayed, and rather unexpected effect of the Sahel drought. Gardelle, J., Hiernaux, P., Kergoat, L., and Grippa, M.: Less rain, more water in ponds: a remote sensing study of the dynamics of surface waters from 1950 to present in pastoral Sahel (Gourma region, Mali), Hydrol. Earth Syst. Sci., 14, 309-324, doi:10.5194/hess-14-309-2010, 201

How satellite rainfall estimate errors may impact rainfed cereal yield simulation in West Africa

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Chapter 9. Paradoxical pond changes in the non-cultivated Sahel

Introduction An unexpected effect of the multi-decadal drought that affected the Sahel from the 1970s onwards has been an increase in surface water flows that have caused various phenomena described collectively as the ‘Sahelian paradox’. This paradox, that can be summarised succinctly as ‘less rainfall but more water in rivers’, is described by Descroix et al. (see Chapter 7). Most of the observations of the paradox were made in a cultivated Sahelian environment and the phenomenon coincided ..

Chapitre 9. Évolutions paradoxales des mares en Sahel non cultivé

Introduction Une conséquence inattendue de la sécheresse multi-décennale qui affecte le Sahel depuis les années 1970 a été l’augmentation des écoulements d’eau en surface, conduisant à différents phénomènes rassemblés sous le terme de « paradoxe sahélien ». Ce paradoxe, qui peut se résumer par la formule lapidaire « moins de pluies, mais plus d’eau dans les rivières », est décrit par Descroix et al. (chap. 7, ce volume). La plupart des observations de ce paradoxe ont été effectuées en milieu ..

Évolutions paradoxales des mares en Sahel non cultivé : diagnostic, causes et conséquences

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